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Tensor Processing Unit
Epoch 10/10
60/60 [==============================] - 3s 42ms/step - loss: 0.0418 -
acc: 0.9876
<tensorflow.python.keras.callbacks.History at 0x7fbb3819bc50>
As you can see, running a simple MNIST model on TPUs is extremely fast. Each
iteration is around 3 seconds even if we have a CNN with 3 convolutions followed
by two dense stages.
Using pretrained TPU models
Google offers a collection of models pretrained with TPUs available on GitHub
TensorFlow/tpu repo (https://github.com/tensorflow/tpu). Models include
image recognition, object detection, low-resource models, machine translation and
language models, speech recognition, and image generation. Whenever it is possible,
my suggestion is to start with a pretrained model [6], and then fine tune it or apply
some form of transfer learning. As of September 2019, the following models are
available:
Image Recognition,
Segmentation, and more
Machine Translation
and Language Models
Speech
Recognition
Image
Generation
Image Recognition
• AmoebaNet-D
Machine Translation
(transformer based)
ASR
Transformer
Image
Transformer
• ResNet-50/101/152/2000
• Inception v2/v3/v4
Sentiment Analysis
DCGAN
Object Detection
• RetinaNet
• Mask R-CNN
(transformer based)
Question Answer
GAN
Image Segmentation
• Mask R-CNN
• DeepLab
Bert
• RetinaNet
Low-Resource Models
• MnasNet
• MobileNet
• SqueezeNet
Table 1: State-of-the-art collection of models pretrained with TPUs available on GitHub
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